Diverse Adversaries for Mitigating Bias in Training

Xudong Han; Timothy Baldwin; Trevor Cohn

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arXiv · 2101.10001

Diverse Adversaries for Mitigating Bias in Training

Xudong Han·Timothy Baldwin·Trevor Cohn

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Abstract

Adversarial learning can learn fairer and less biased models of language than standard methods. However, current adversarial techniques only partially mitigate model bias, added to which their training procedures are often unstable. In this paper, we propose a novel approach to adversarial learning based on the use of multiple diverse discriminators, whereby discriminators are encouraged to learn orthogonal hidden representations from one another. Experimental results show that our method substantially improves over standard adversarial removal methods, in terms of reducing bias and the stability of training.

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Xudong Han, Timothy Baldwin, Trevor Cohn. 2021-01-25. Diverse Adversaries for Mitigating Bias in Training. https://arxiv.org/abs/2101.10001

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